Study on Large-Scale Urban Water Distribution Network Computation Method Based on a GPU Framework

IF 3 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Water Pub Date : 2024-09-18 DOI:10.3390/w16182642
Rongbin Zhang, Jingming Hou, Jingsi Li, Tian Wang, Muhammad Imran
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引用次数: 0

Abstract

Large-scale urban water distribution network simulation plays a critical role in the construction, monitoring, and maintenance of urban water distribution systems. However, during the simulation process, matrix inversion calculations generate a large amount of computational data and consume significant amounts of time, posing challenges for practical applications. To address this issue, this paper proposes a parallel gradient calculation algorithm based on GPU hardware and the CUDA Toolkit library and compares it with the EPANET model and a model based on CPU hardware and the Armadillo library. The results show that the GPU-based model not only achieves a precision level very close to the EPANET model, reaching 99% accuracy, but also significantly outperforms the CPU-based model. Furthermore, during the simulation, the GPU architecture is able to efficiently handle large-scale data and achieve faster convergence, significantly reducing the overall simulation time. Particularly in handling larger-scale water distribution networks, the GPU architecture can improve computational efficiency by up to 13 times. Further analysis reveals that different GPU models exhibit significant differences in computational efficiency, with memory capacity being a key factor affecting performance. GPU devices with larger memory capacity demonstrate higher computational efficiency when processing large-scale water distribution networks. This study demonstrates the advantages of GPU acceleration technology in the simulation of large-scale urban water distribution networks and provides important theoretical and technical support for practical applications in this field. By carefully selecting and configuring GPU devices, the computational efficiency of large-scale water distribution networks can be significantly improved, providing more efficient solutions for future urban water resource management and planning.
基于 GPU 框架的大规模城市配水管网计算方法研究
大规模城市配水管网模拟在城市配水系统的建设、监测和维护中发挥着至关重要的作用。然而,在仿真过程中,矩阵反演计算会产生大量计算数据并消耗大量时间,这给实际应用带来了挑战。为解决这一问题,本文提出了一种基于 GPU 硬件和 CUDA 工具包库的并行梯度计算算法,并将其与 EPANET 模型以及基于 CPU 硬件和 Armadillo 库的模型进行了比较。结果表明,基于 GPU 的模型不仅达到了与 EPANET 模型非常接近的精度水平,准确率达到 99%,而且明显优于基于 CPU 的模型。此外,在仿真过程中,GPU 架构能够有效地处理大规模数据并实现更快的收敛,从而大大缩短了整体仿真时间。特别是在处理更大规模的配水管网时,GPU 架构可将计算效率提高 13 倍。进一步的分析表明,不同的 GPU 模型在计算效率方面存在显著差异,而内存容量是影响性能的关键因素。内存容量较大的 GPU 设备在处理大规模配水管网时表现出更高的计算效率。这项研究证明了 GPU 加速技术在大规模城市配水管网仿真中的优势,并为该领域的实际应用提供了重要的理论和技术支持。通过精心选择和配置 GPU 设备,可以显著提高大规模配水管网的计算效率,为未来城市水资源管理和规划提供更高效的解决方案。
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来源期刊
Water
Water WATER RESOURCES-
CiteScore
5.80
自引率
14.70%
发文量
3491
审稿时长
19.85 days
期刊介绍: Water (ISSN 2073-4441) is an international and cross-disciplinary scholarly journal covering all aspects of water including water science and technology, and the hydrology, ecology and management of water resources. It publishes regular research papers, critical reviews and short communications, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles. Computed data or files regarding the full details of the experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
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